BLOCKBENCH: A Framework for Analyzing Private Blockchains

Research Paper Presentation

Cesaire Tobias

2025-05-02

Presentation Overview

  1. Introduction to Private Blockchains
  2. The Need for BLOCKBENCH
  3. Framework Architecture
  4. Benchmark Workloads
  5. Evaluation Methodology
  6. Performance Results
  7. Key Findings
  8. Implications for Industry
  9. Future Directions
  10. Conclusion

Introduction to Private Blockchains

  • Private Blockchains: Permissioned networks where participants are identified
  • Target Applications:
    • Enterprise systems
    • Consortium networks
    • Supply chains
  • Key Platforms:
    • Ethereum
    • Hyperledger Fabric
    • Parity

For more on Blockchain Types, see Appendix

The Need for BLOCKBENCH

  • Lack of Standard Evaluation Framework
  • Difficult to Compare Different Systems
  • Gap Between Industry Claims and Reality
  • No Baseline Comparison with Traditional Databases

Framework Architecture

flowchart TD
    %% Main title
    title["BlockBench Framework Architecture"]
    
    %% Main Layers
    WL[Workload Layer\nYCSB, Smallbank, EtherId, DoNothing]
    EE[Execution Engine Layer\nSmart Contracts, Virtual Machines]
    CL[Consensus Layer\nPoW, PBFT, PoS, Raft]
    DM[Data Model Layer\nKey-Value Store, Account/State Model]
    BP[Blockchain Platforms\nEthereum, Hyperledger Fabric, Parity, Quorum]
    
    %% Left side - Drivers
    DI[Drivers & Interfaces]
    D1[Macro Benchmarks]
    D2[Micro Benchmarks]
    D3[Smart Contracts]
    D4[Client APIs]
    D5[Asynchronous]
    
    %% Right side - Metrics
    PM[Performance Metrics]
    M1[Throughput]
    M2[Latency]
    M3[Scalability]
    M4[Fault Tolerance]
    M5[Security]
    
    %% Connections for main flow
    title --> WL
    WL --> EE
    EE --> CL
    CL --> DM
    DM --> BP
    
    %% Connections for drivers
    DI --> D1
    DI --> D2
    DI --> D3
    DI --> D4
    DI --> D5
    
    D1 --- WL
    D2 --- EE
    D3 --- EE
    D4 --- CL
    D5 --- DM
    
    %% Connections for metrics
    PM --> M1
    PM --> M2
    PM --> M3
    PM --> M4
    PM --> M5
    
    M1 --- WL
    M2 --- EE
    M3 --- CL
    M4 --- CL
    M5 --- DM
    
    %% Styling
    classDef workload fill:#d1e7dd,stroke:#198754,stroke-width:2px
    classDef execution fill:#cfe2ff,stroke:#0d6efd,stroke-width:2px
    classDef consensus fill:#f8d7da,stroke:#dc3545,stroke-width:2px
    classDef datamodel fill:#fff3cd,stroke:#ffc107,stroke-width:2px
    classDef platforms fill:#e2e3e5,stroke:#6c757d,stroke-width:2px
    classDef drivers fill:#e2e3e5,stroke:#6c757d,stroke-width:1px
    classDef metrics fill:#e2e3e5,stroke:#6c757d,stroke-width:1px
    classDef title font-size:18px,font-weight:bold
    
    class WL workload
    class EE execution
    class CL consensus
    class DM datamodel
    class BP platforms
    class DI,D1,D2,D3,D4,D5 drivers
    class PM,M1,M2,M3,M4,M5 metrics
    class title title

Benchmark Workloads

Workload Description Tests
YCSB Key-value operations Read/write/scan operations
Smallbank Banking transactions Account transfers, balance checks
EtherId Name registration Complex state operations
Donothing Empty blocks Consensus overhead

Evaluation Methodology

  • Systems Under Test:
    • Ethereum (PoW)
    • Hyperledger Fabric (PBFT)
    • Parity (PoA)
    • H-Store (traditional distributed database)
  • Controlled Environment: 48-node cluster
  • Statistical Rigor: Multiple runs, confidence intervals
  • Parameter Optimization: Each system configured optimally
graph TD
    A[Setup] --> B[Systems: Ethereum, Hyperledger, Parity, H-Store]
    B --> C[48-node Cluster]
    C --> D[Optimize Parameters]
    D --> E[Run Benchmarks]
    E --> F[Collect Metrics]
    F --> G[Statistical Analysis]
    style A fill:#0288d1,stroke:#0277bd,color:#fff
    style E fill:#03a9f4,stroke:#0288d1,color:#fff
    style G fill:#03a9f4,stroke:#0288d1,color:#fff

Performance Results: Throughput

  • Blockchain vs Database:
    • H-Store: 10,000+ TPS
    • Private Blockchains: 1,000-3,000 TPS peak
  • Platform Comparison:
    • Ethereum: Moderate but consistent
    • Hyperledger: Higher peaks, more variability
    • Parity: Performance varies by workload

Performance Results: Latency

  • Transaction Confirmation Time:
    • H-Store: Milliseconds
    • Ethereum: 3-10 seconds
    • Hyperledger: 100-800 milliseconds
    • Parity: 200-1000 milliseconds
  • Consensus Protocol Impact:
    • PoW (Ethereum): Highest latency
    • PBFT (Hyperledger): Lower but scaling issues
    • PoA (Parity): Varies with validator count

Performance Results: Scalability

  • Negative Scalability:
    • Performance degrades as nodes increase
    • 30-50% reduction when doubling nodes
  • Consensus Bottlenecks:
    • PBFT: O(n²) message complexity
    • PoW: Network propagation overhead
  • Contrast with Databases:
    • H-Store: Near-linear scaling (positive)

Layer-Specific Findings

  • Consensus Layer: 80-90% of total latency
  • Data Model Layer:
    • Account-based (Ethereum): Better for read-heavy workloads
    • UTXO-based (Hyperledger): Better for write-intensive applications
  • Execution Layer:
    • 10-100x overhead compared to native execution
    • Inefficient memory management
    • Limited optimization capabilities

Key Findings and Implications

  • Performance Gap: Private blockchains significantly underperform databases
  • Negative Scalability: Adding nodes decreases performance
  • Execution Inefficiency: Smart contracts introduce major overhead
  • Consensus Dominance: Protocol choice is the primary performance determinant
  • Data Model Impact: Selection affects workload suitability
%%{init: {'theme': 'default', 'themeVariables': {'fontSize': '18px'}}}%%
graph TD
    A[Key BLOCKBENCH Findings] --> B[Performance Gap]
    A --> C[Negative Scalability]
    A --> D[Execution Inefficiency]
    A --> E[Consensus Dominance]
    A --> F[Data Model Impact]
    B --> B1[10-100x slower than databases]
    C --> C1[Performance decreases with node count]
    D --> D1[Smart contracts add 10-100x overhead]
    E --> E1[Consensus protocol is primary bottleneck]
    F --> F1[Data model should match workload]
    style A fill:#0288d1,stroke:#0277bd,color:#fff

Implications for Industry

Industry Requirements Blockchain Readiness
Financial Services High throughput, low latency Limited - Hybrid solutions recommended
Supply Chain Scalability, moderate throughput Moderate - Careful design needed
Healthcare Confidentiality, moderate latency Promising - With optimization
Real Estate Security, lower throughput Good fit - Current performance adequate

Conclusion

  • BLOCKBENCH Contribution: First systematic framework for evaluation
  • Current State: Promising technology with significant limitations
  • Path Forward:
    • Consensus protocol innovation
    • Execution optimization
    • Data model hybridization
    • Architecture rethinking
%%{init: {'theme': 'default', 'themeVariables': {'fontSize': '18px'}}}%%
graph TD
    A[Path Forward] --> B[Consensus Innovation]
    B --> C[Execution Optimization]
    C --> D[Data Model Hybridization]
    D --> E[Architecture Rethinking]
    B --> B1[Layered protocols]
    C --> C1[JIT compilation]
    D --> D1[Hybrid approaches]
    E --> E1[Database integration]
    style A fill:#0288d1,stroke:#0277bd,color:#fff

Thank You

Questions?

Appendix

The following slides contain supplementary information including charts, diagrams, references, and detailed results from the paper.

Additional Resources

BLOCKBENCH Paper

View BLOCKBENCH Paper

Blockchain Types

%%{init: {'theme': 'default', 'themeVariables': {'fontSize': '22px'}}}%%
graph TD
    A[Blockchain Types] --> B[Public Blockchain]
    A --> C[Private Blockchain]
    B --> B1[Open Access: Anyone can join]
    B --> B2[Examples: Bitcoin, Ethereum]
    B --> B3[High Decentralization]
    C --> C1[Permissioned: Known participants]
    C --> C2[Examples: Hyperledger, Parity]
    C --> C3[Enterprise Use: Supply chain, finance]
    style A fill:#0288d1,stroke:#0277bd,color:#fff
    style B fill:#03a9f4,stroke:#0288d1,color:#fff
    style C fill:#03a9f4,stroke:#0288d1,color:#fff

Need for Standard Framework

%%{init: {'theme': 'default', 'themeVariables': {'fontSize': '22px'}}}%%
graph TD
    subgraph Current State
        A[Blockchain Systems] --> B[Vendor Claims]
        A --> C[Custom Evaluations]
        B --> D[Inconsistent Metrics]
        C --> D
        D --> E[Limited Comparability]
        D --> F[No Database Benchmark]
        D --> G[Unreliable Insights]
    end
    subgraph With BLOCKBENCH
        H[Blockchain Systems] --> I[BLOCKBENCH Framework]
        I --> J[Standardized Metrics]
        I --> K[Cross-Platform Comparison]
        I --> L[Database Benchmarking]
        I --> M[Objective Insights]
    end
    E & F & G --> N[Need for BLOCKBENCH]
    N --> I
    style I fill:#0288d1,stroke:#0277bd,color:#fff
    style N fill:#03a9f4,stroke:#0288d1,color:#fff

BLOCKBENCH Framework

%%{init: {'theme': 'default', 'themeVariables': {'fontSize': '22px'}}}%%
graph TD
    A[BLOCKBENCH Framework] --> B[Consensus Layer]
    A --> C[Data Model Layer]
    A --> D[Execution Layer]
    B --> B1[Transaction Agreement]
    C --> C1[State Management]
    D --> D1[Smart Contract Execution]
    A --> E[Benchmark Interface]
    E --> F[Metrics: Throughput, Latency, Scalability, Fault Tolerance]
    style A fill:#0288d1,stroke:#0277bd,color:#fff
    style E fill:#03a9f4,stroke:#0288d1,color:#fff

Benchmark Workloads Diagram

%%{init: {'theme': 'default', 'themeVariables': {'fontSize': '22px'}}}%%
graph TD
    A[Workloads] --> B[YCSB]
    A --> C[Smallbank]
    A --> D[EtherId]
    A --> E[Donothing]
    B --> B1[Key-value operations]
    C --> C1[Banking transactions]
    D --> D1[Name registration]
    E --> E1[Empty blocks]
    style A fill:#0288d1,stroke:#0277bd,color:#fff
    style B fill:#03a9f4,stroke:#0288d1,color:#fff
    style C fill:#03a9f4,stroke:#0288d1,color:#fff
    style D fill:#03a9f4,stroke:#0288d1,color:#fff
    style E fill:#03a9f4,stroke:#0288d1,color:#fff

Key Findings

%%{init: {'theme': 'default', 'themeVariables': {'fontSize': '22px'}}}%%
graph TD
    A[Key Findings] --> B[Performance Gap]
    A --> C[Negative Scalability]
    A --> D[Execution Inefficiency]
    A --> E[Consensus Dominance]
    A --> F[Data Model Impact]
    B --> B1[10-100x slower than databases]
    C --> C1[Performance decreases with node count]
    D --> D1[Smart contracts add 10-100x overhead]
    E --> E1[Consensus protocol is primary bottleneck]
    F --> F1[Data model should match workload]
    style A fill:#0288d1,stroke:#0277bd,color:#fff

Industry Readiness

  • Blockchain Readiness:
    • Varies by industry needs
    • Requires optimization

YCSB Benchmark Results

  • Read Operations:
    • Ethereum: 300-400 ops/sec
    • Hyperledger: 1000-1200 ops/sec
    • Parity: 800-900 ops/sec
    • H-Store: 10,000+ ops/sec
  • Write Operations:
    • 30-50% slower than reads

Smallbank Benchmark Results

  • Transaction Throughput:
    • Ethereum: 250-300 TPS
    • Hyperledger: 700-850 TPS
    • Parity: 500-650 TPS
    • H-Store: 8,000-9,000 TPS
  • Consistency Testing:
    • All systems maintain ACID properties

EtherId Benchmark Results

  • Complex Smart Contract Operations:
    • Ethereum: 40-50 ops/sec
    • Hyperledger: 100-150 ops/sec
    • Parity: 70-90 ops/sec
  • Execution Overhead:
    • VM execution: 40-60%
    • State access: 15-25%
    • Memory management: 10-20%

Consensus Protocols

Protocol Description Used By Characteristics
PoW Cryptographic puzzles Ethereum High security, high latency
PBFT Multi-round voting Hyperledger Fabric Lower latency, O(n²) complexity
PoA Authorized validators Parity Low latency, centralization concerns

System Architecture Comparison

%%{init: {'theme': 'default', 'themeVariables': {'fontSize': '22px'}}}%%
graph TD
    A[Systems] --> B[Ethereum]
    A --> C[Hyperledger]
    A --> D[Parity]
    B --> B1[Account-based]
    B --> B2[PoW]
    B --> B3[EVM]
    C --> C1[UTXO-based]
    C --> C2[PBFT]
    C --> C3[Docker]
    D --> D1[Account-based]
    D --> D2[PoA]
    D --> D3[EVM]
    style A fill:#0288d1,stroke:#0277bd,color:#fff
System Data Model Consensus Execution Environment
Ethereum Account-based PoW EVM
Hyperledger UTXO-based PBFT Docker container
Parity Account-based PoA EVM

Future Research Directions

  • Layered Consensus Protocols: Tailored consensus per operation
  • Just-In-Time Compilation: Optimize smart contracts
  • Sharding Approaches: Improve scalability
  • Hybrid Architecture: Combine blockchain and databases
  • Hardware Acceleration: Enhance cryptographic performance

References

  • Dinh, T. T. A., et al. (2017). BLOCKBENCH: A framework for analyzing private blockchains. In Proceedings of the 2017 ACM International Conference on Management of Data (pp. 1085-1100).
  • Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Bitcoin.org
  • Wood, G. (2014). Ethereum: A secure decentralised generalised transaction ledger. Ethereum project yellow paper.
  • Androulaki, E., et al. (2018). Hyperledger fabric: a distributed operating system for permissioned blockchains. In Proceedings of the Thirteenth EuroSys Conference.

Definitions

Term Definition
Block Container of transactions
Blockchain Distributed ledger of linked blocks
Consensus Protocol Mechanism for node agreement
EVM Ethereum Virtual Machine for smart contracts
Latency Time to confirm a transaction
Private Blockchain Permissioned blockchain with known participants
Scalability Performance change with network size
Smart Contract Self-executing blockchain code
Throughput Transactions per second
UTXO Unspent Transaction Output data model

Financial Software Engineering (ECO5040S) - Assignment 1